The present invention relates generally to passive signal sensing and signal processing, and more particularly, to signal processing incorporating signal tracking, estimation, and removal processing using maximum a posteriori and sequential signal detection methods.
Signal processing hardware and software has improved to the point where it is very difficult, if not impossible, for sonar operators to manually detect and track all desirable signals that are available. Traditional signal detection and estimation processes are achieved by the integration of spectral bins at constant frequency over some time period T. This approach results in poor estimation of the parameters associated with signals that do not exhibit pure tonal characteristics.
Furthermore, an underlying processing assumption has been that there always exists a signal in the input data. However, this condition cannot always be guaranteed in applications in which the input data is often noise-only. To circumvent this discrepancy, in an existing maximum a posteriori (MAP) processing system developed by the assignee of the present invention, the decision regarding the presence of absence of a signal was deferred until the line linking and clustering stages. This design was conceived with the assumption that noise tracks (signal absence) would not be successfully linked and clustered and therefore would eventually be purged, thereby effectively accomplishing the desired signal detection. Unfortunately, this preconception has proven to be incorrect by computer simulations which showed noise tracks were often erroneously linked to existing signal tracks due to the likeness of their signal features.
This result has led to the conclusion that eliminating noise tracks at the MAP output is a necessary processing step for achieving an acceptable performance. This means that the MAP processing must be extended beyond its original capabilities to include signal detection logic in its procedure. To achieve this, the MAP output must be statistically characterize under noise-only and signal-plus-noise conditions.
For signal detection applications, very little was previously known about the MAP algorithm in terms of its quantitative performance, such as minimum detectable signal-to-noise ratio (SNR), probability of detection (pd). probability of false alarm (pfa), and performance sensitivity to input spectral statistics. This has caused problems in selecting correct thresholds to yield desired detection performance.